import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
				from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     List[str]		)      ->	str:
				_lowerCamelCase				   =  self.config_class(**self.inputs_dict		)
				self.parent.assertTrue(hasattr(snake_case__     ,  'tf_padding'		)		)
				self.parent.assertTrue(hasattr(snake_case__     ,  'depth_multiplier'		)		)





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     int=1_3     ,  snake_case__     :     Optional[int]=3     ,  snake_case__     :     str=3_2     ,  snake_case__     :     Union[str, Any]=0.25     ,  snake_case__     :     Union[str, Any]=8     ,  snake_case__     :     Tuple=8     ,  snake_case__     :     int=6     ,  snake_case__     :     List[Any]=3_2     ,  snake_case__     :     Optional[Any]=True     ,  snake_case__     :     List[Any]=True     ,  snake_case__     :     List[str]=True     ,  snake_case__     :     Optional[int]="relu6"     ,  snake_case__     :     List[str]=1_2_8_0     ,  snake_case__     :     Any=0.1     ,  snake_case__     :     Union[str, Any]=0.02     ,  snake_case__     :     List[str]=True     ,  snake_case__     :     List[Any]=True     ,  snake_case__     :     Any=1_0     ,  snake_case__     :     int=None     ,  )      ->	List[Any]:
				_lowerCamelCase				   =  parent
				_lowerCamelCase				   =  batch_size
				_lowerCamelCase				   =  num_channels
				_lowerCamelCase				   =  image_size
				_lowerCamelCase				   =  depth_multiplier
				_lowerCamelCase				   =  depth_divisible_by
				_lowerCamelCase				   =  min_depth
				_lowerCamelCase				   =  expand_ratio
				_lowerCamelCase				   =  tf_padding
				_lowerCamelCase				   =  output_stride
				_lowerCamelCase				   =  first_layer_is_expansion
				_lowerCamelCase				   =  finegrained_output
				_lowerCamelCase				   =  hidden_act
				_lowerCamelCase				   =  last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
				_lowerCamelCase				   =  classifier_dropout_prob
				_lowerCamelCase				   =  use_labels
				_lowerCamelCase				   =  is_training
				_lowerCamelCase				   =  num_labels
				_lowerCamelCase				   =  initializer_range
				_lowerCamelCase				   =  scope
		def   _snake_case       (       self     :     int		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				_lowerCamelCase				   =  None
				_lowerCamelCase				   =  None
				if self.use_labels:
						_lowerCamelCase				   =  ids_tensor([self.batch_size]     ,  self.num_labels		)
						_lowerCamelCase				   =  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,  self.num_labels		)

				_lowerCamelCase				   =  self.get_config()

				return config, pixel_values, labels, pixel_labels
		def   _snake_case       (       self     :     str		)      ->	Union[str, Any]:
				return MobileNetVaConfig(
				    num_channels=self.num_channels     ,  image_size=self.image_size     ,  depth_multiplier=self.depth_multiplier     ,  depth_divisible_by=self.depth_divisible_by     ,  min_depth=self.min_depth     ,  expand_ratio=self.expand_ratio     ,  output_stride=self.output_stride     ,  first_layer_is_expansion=self.first_layer_is_expansion     ,  finegrained_output=self.finegrained_output     ,  hidden_act=self.hidden_act     ,  tf_padding=self.tf_padding     ,  classifier_dropout_prob=self.classifier_dropout_prob     ,  initializer_range=self.initializer_range     ,  )
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     str		)      ->	int:
				_lowerCamelCase				   =  MobileNetVaModel(config=snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape     ,  (
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )
				self.parent.assertEqual(
				    result.pooler_output.shape     ,  (self.batch_size, self.last_hidden_size)     ,  )
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Tuple     ,  snake_case__     :     List[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     Any		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  MobileNetVaForImageClassification(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     str		)      ->	List[Any]:
				_lowerCamelCase				   =  self.num_labels
				_lowerCamelCase				   =  MobileNetVaForSemanticSegmentation(snake_case__		)
				model.to(snake_case__		)
				model.eval()
				_lowerCamelCase				   =  model(snake_case__		)
				self.parent.assertEqual(
				    result.logits.shape     ,  (
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )
				_lowerCamelCase				   =  model(snake_case__     ,  labels=snake_case__		)
				self.parent.assertEqual(
				    result.logits.shape     ,  (
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )     ,  )



		def   _snake_case       (       self     :     Any		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.prepare_config_and_inputs()
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  config_and_inputs
				_lowerCamelCase				   =  {'pixel_values': pixel_values}
				return config, inputs_dict





@require_torch
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   (
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		lowerCAmelCase_     =   (
		    {
		        'feature-extraction': MobileNetVaModel,
		        'image-classification': MobileNetVaForImageClassification,
		        'image-segmentation': MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		lowerCAmelCase_     =   False
		def   _snake_case       (       self     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  MobileNetVaModelTester(self		)
				_lowerCamelCase				   =  MobileNetVaConfigTester(self     ,  config_class=snake_case__     ,  has_text_modality=snake_case__		)
		def   _snake_case       (       self     :     str		)      ->	str:
				self.config_tester.run_common_tests()
		@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'		)
		def   _snake_case       (       self     :     List[str]		)      ->	Tuple:
				pass
		@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'		)
		def   _snake_case       (       self     :     List[Any]		)      ->	int:
				pass
		@unittest.skip(reason='MobileNetV2 does not output attentions'		)
		def   _snake_case       (       self     :     Any		)      ->	Optional[int]:
				pass
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  model_class(snake_case__		)
						_lowerCamelCase				   =  inspect.signature(model.forward		)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowerCamelCase				   =  [*signature.parameters.keys()]

						_lowerCamelCase				   =  ['pixel_values']
						self.assertListEqual(arg_names[:1]     ,  snake_case__		)
		def   _snake_case       (       self     :     Optional[int]		)      ->	Any:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case__		)
		def   _snake_case       (       self     :     Dict		)      ->	Any:
				def check_hidden_states_output(snake_case__     :     int     ,  snake_case__     :     Tuple     ,  snake_case__     :     Tuple		):
						_lowerCamelCase				   =  model_class(snake_case__		)
						model.to(snake_case__		)
						model.eval()

						with torch.no_grad():
								_lowerCamelCase				   =  model(**self._prepare_for_class(snake_case__     ,  snake_case__		)		)

						_lowerCamelCase				   =  outputs.hidden_states

						_lowerCamelCase				   =  1_6
						self.assertEqual(len(snake_case__		)     ,  snake_case__		)

				_lowerCamelCase   ,							_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						_lowerCamelCase				   =  True
						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_lowerCamelCase				   =  True

						check_hidden_states_output(snake_case__     ,  snake_case__     ,  snake_case__		)
		def   _snake_case       (       self     :     List[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*snake_case__		)
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				_lowerCamelCase				   =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__		)



		@slow
		def   _snake_case       (       self     :     Tuple		)      ->	Union[str, Any]:
				for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowerCamelCase				   =  MobileNetVaModel.from_pretrained(snake_case__		)
						self.assertIsNotNone(snake_case__		)



def 	lowerCamelCase					(	)   ->    Dict:
		_lowerCamelCase				   =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
		return image





@require_torch
@require_vision
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		@cached_property
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[Any]:
				return (
				    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'		) if is_vision_available() else None
				)
		@slow
		def   _snake_case       (       self     :     Optional[Any]		)      ->	List[str]:
				_lowerCamelCase				   =  MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'		).to(snake_case__		)

				_lowerCamelCase				   =  self.default_image_processor
				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 1_0_0_1)		)
				self.assertEqual(outputs.logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor([0.2445, -1.1993, 0.1905]		).to(snake_case__		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  snake_case__     ,  atol=1e-4		)		)



		@slow
		def   _snake_case       (       self     :     List[Any]		)      ->	List[Any]:
				_lowerCamelCase				   =  MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)
				_lowerCamelCase				   =  model.to(snake_case__		)

				_lowerCamelCase				   =  MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)

				_lowerCamelCase				   =  prepare_img()
				_lowerCamelCase				   =  image_processor(images=snake_case__     ,  return_tensors='pt'		).to(snake_case__		)

				# forward pass
				with torch.no_grad():
						_lowerCamelCase				   =  model(**snake_case__		)
				_lowerCamelCase				   =  outputs.logits

				# verify the logits
				_lowerCamelCase				   =  torch.Size((1, 2_1, 6_5, 6_5)		)
				self.assertEqual(logits.shape     ,  snake_case__		)

				_lowerCamelCase				   =  torch.tensor(
				    [
				        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
				        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
				        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
				    ]     ,  device=snake_case__     ,  )

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,  snake_case__     ,  atol=1e-4		)		)
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]							)   ->    Optional[Any]:
		_lowerCamelCase				   =  3_84
		_lowerCamelCase				   =  7
		if "tiny" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 6, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "small" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "base" in model_name:
				_lowerCamelCase				   =  1_28
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (4, 8, 16, 32)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  5_12
		elif "large" in model_name:
				_lowerCamelCase				   =  1_92
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (6, 12, 24, 48)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  7_68

		# set label information
		_lowerCamelCase				   =  1_50
		_lowerCamelCase				   =  'huggingface/label-files'
		_lowerCamelCase				   =  'ade20k-id2label.json'
		_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
		_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
		_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		_lowerCamelCase				   =  SwinConfig(
		    embed_dim=UpperCamelCase			,       depths=UpperCamelCase			,       num_heads=UpperCamelCase			,       window_size=UpperCamelCase			,       out_features=['stage1', 'stage2', 'stage3', 'stage4']			,       )
		_lowerCamelCase				   =  UperNetConfig(
		    backbone_config=UpperCamelCase			,       auxiliary_in_channels=UpperCamelCase			,       num_labels=UpperCamelCase			,       idalabel=UpperCamelCase			,       labelaid=UpperCamelCase			,       )

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  []

		# fmt: off
		# stem
		rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')							)
		rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')							)
		rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')							)
		rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')							)
		# stages
		for i in range(len(config.backbone_config.depths							)							):
				for j in range(config.backbone_config.depths[i]							):
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")							)

				if i < 3:
						rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")							)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")							)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")							)

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
		# fmt: on

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Any							)   ->    str:
		_lowerCamelCase				   =  dct.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Tuple							)   ->    str:
		_lowerCamelCase				   =  [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
		for i in range(len(backbone_config.depths							)							):
				_lowerCamelCase				   =  num_features[i]
				for j in range(backbone_config.depths[i]							):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""							)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""							)
						# next, add query, keys and values (in that order) to the state dict
						_lowerCamelCase				   =  in_proj_weight[:dim, :]
						_lowerCamelCase				   =  in_proj_bias[: dim]
						_lowerCamelCase				   =  in_proj_weight[
						    dim : dim * 2, :
						]
						_lowerCamelCase				   =  in_proj_bias[
						    dim : dim * 2
						]
						_lowerCamelCase				   =  in_proj_weight[
						    -dim :, :
						]
						_lowerCamelCase				   =  in_proj_bias[-dim :]
						# fmt: on


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[int]:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3], :].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    int:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, :, [0, 2, 1, 3]].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)

		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    List[str]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[[0, 2, 1, 3], :].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3]].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Dict							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  {
		    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
		    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
		    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
		    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
		}
		_lowerCamelCase				   =  model_name_to_url[model_name]
		_lowerCamelCase				   =  torch.hub.load_state_dict_from_url(UpperCamelCase			,       map_location='cpu'			,       file_name=UpperCamelCase							)[
		    'state_dict'
		]

		for name, param in state_dict.items():
				print(UpperCamelCase			,       param.shape							)

		_lowerCamelCase				   =  get_upernet_config(UpperCamelCase							)
		_lowerCamelCase				   =  UperNetForSemanticSegmentation(UpperCamelCase							)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
				if "bn" in key:
						_lowerCamelCase				   =  key.replace('bn'			,       'batch_norm'							)
				_lowerCamelCase				   =  val

		# rename keys
		_lowerCamelCase				   =  create_rename_keys(UpperCamelCase							)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		read_in_q_k_v(UpperCamelCase			,       config.backbone_config							)

		# fix downsample parameters
		for key, value in state_dict.items():
				if "downsample" in key:
						if "reduction" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_reduction_order(UpperCamelCase							)
						if "norm" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_norm_order(UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)

		# verify on image
		_lowerCamelCase				   =  'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							).convert('RGB'							)

		_lowerCamelCase				   =  SegformerImageProcessor()
		_lowerCamelCase				   =  processor(UpperCamelCase			,       return_tensors='pt'							).pixel_values

		with torch.no_grad():
				_lowerCamelCase				   =  model(UpperCamelCase							)
				_lowerCamelCase				   =  outputs.logits

		print(logits.shape							)
		print('First values of logits:'			,       logits[0, 0, :3, :3]							)
		# assert values
		if model_name == "upernet-swin-tiny":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]							)
		elif model_name == "upernet-swin-small":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]							)
		elif model_name == "upernet-swin-base":
				_lowerCamelCase				   =  torch.tensor(
				    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]							)
		elif model_name == "upernet-swin-large":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]							)
		print('Logits:'			,       outputs.logits[0, 0, :3, :3]							)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]			,       UpperCamelCase			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(UpperCamelCase							)
				print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				print(F"""Pushing model and processor for {model_name} to hub"""							)
				model.push_to_hub(F"""openmmlab/{model_name}"""							)
				processor.push_to_hub(F"""openmmlab/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-swin-tiny',
				    type=str,
				    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
				    help='Name of the Swin + UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)